AI-Powered Plant Disease Classification: Innovating for Sustainable Agriculture

Authors

  • Arnav Bansal

DOI:

https://doi.org/10.36676/jrps.2023-v14i5-09

Keywords:

research, overfitting, management of plant

Abstract

This research explores the development and application of artificial intelligence (AI) models in Python for plant disease classification. Using a large training dataset with over 50,000 images representing various plant conditions, the study highlights the effectiveness of AI and computer vision by achieving approximately 86% accuracy in correctly identifying the plant disease. It demonstrates a balanced approach to maintaining accuracy while avoiding overfitting, underscoring AI's potential in agriculture.

References

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He, Kaiming, et al. "Deep Residual Learning for Image Recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. DOI: https://doi.org/10.1109/CVPR.2016.90

Kingma, Diederik P., and Jimmy Ba. "Adam: A Method for Stochastic Optimization." arXiv preprint arXiv:1412.6980, 2014.

Bottou, Léon. "Large-Scale Machine Learning with Stochastic Gradient Descent." Proceedings of COMPSTAT'2010, Physica-Verlag HD, 2010, pp. 177-186. DOI: https://doi.org/10.1007/978-3-7908-2604-3_16

Mohanty, Sharada P., David P. Hughes, and Marcel Salathé. "Using Deep Learning for Image-Based Plant Disease Detection." Frontiers in Plant Science, vol. 7, 2016, p. 1419, Frontiers. DOI: https://doi.org/10.3389/fpls.2016.01419

Plant Methods Editors. "Plant Diseases and Pests Detection Based on Deep Learning: A Review." Plant Methods, vol. 17, no. 22, 2021. DOI: https://doi.org/10.1186/s13007-021-00722-9

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Published

31-12-2023

How to Cite

Arnav Bansal. (2023). AI-Powered Plant Disease Classification: Innovating for Sustainable Agriculture. International Journal for Research Publication and Seminar, 14(5), 55–59. https://doi.org/10.36676/jrps.2023-v14i5-09